用事件相机和脉冲网络实现太空飞行器6维姿态估计算法
Spiking monocular event based 6D pose estimation for space application
- 基于事件相机与脉冲神经网络，构建端到端的全事件感知姿态估计模型
- 在真实数据集SEENIC上实现21cm位置误差和14°旋转误差
- 为低功耗嵌入式航天器姿态估计提供新思路，适合空间任务应用
随着在轨服务（OOS）和主动碎片清除（ADR）任务需求增长，研究人员正利用深度学习提升航天器姿态估计精度并寻找高效解决方案。得益于类脑低功耗技术的发展，如脉冲神经网络与事件相机，我们首次探索了全事件处理方案在航天姿态估计中的可行性。本文提出首个事件相机采集的真实事件帧数据集SEENIC，展示了首个针对该场景的事件基解决方案。所提出的轻量级脉冲端到端网络S2E2，在真实测试中达到21cm位置误差和14°旋转误差，是迈向嵌入式航天器全事件姿态估计的重要一步。
原文摘要 · Abstract (English)
With the growing interest in on On-orbit servicing (OOS) and Active Debris Removal (ADR) missions, spacecraft poses estimation algorithms are being developed using deep learning to improve the precision of this complex task and find the most efficient solution. With the advances of bio-inspired low-power solutions, such a spiking neural networks and event-based processing and cameras, and their recent work for space applications, we propose to investigate the feasibility of a fully event-based solution to improve event-based pose estimation for spacecraft. In this paper, we address the first event-based dataset SEENIC with real event frames captured by an event-based camera on a testbed. We show the methods and results of the first event-based solution for this use case, where our small spiking end-to-end network (S2E2) solution achieves interesting results over 21cm position error and 14degree rotation error, which is the first step towards fully event-based processing for embedded spacecraft pose estimation.
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